



'''simple docstring'''






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowerCAmelCase:   List[Any]					=		logging.get_logger(__name__)


lowerCAmelCase:   List[Any]					=		r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'

class 				a__(   lowerCamelCase__      ):







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Optional[Any]		,     __snake_case					:    torch.LongTensor		,     __snake_case					:    torch.FloatTensor		,     **__snake_case					:    str      ):
      raise NotImplementedError('StoppingCriteria needs to be subclassed'      )

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    List[Any]		,     __snake_case					:    int		,     __snake_case					:    Optional[int] = None      ):
      a		:   Optional[Any]	  =	max_length
      a		:   List[str]	  =	max_position_embeddings







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    List[Any]		,     __snake_case					:    torch.LongTensor		,     __snake_case					:    torch.FloatTensor		,     **__snake_case					:    Union[str, Any]      ):
      a		:   Any	  =	input_ids.shape[-1]
      a		:   Union[str, Any]	  =	cur_len >= self.max_length
      if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
        logger.warning_once(
            'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
            F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
            'exceptions, performance degradation, or nothing at all.'      )
      return is_done

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Tuple		,     __snake_case					:    int		,     __snake_case					:    int      ):
      warnings.warn(
          'The class `MaxNewTokensCriteria` is deprecated. '
          F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
          'with `max_length = start_length + max_new_tokens` instead.'		,     __snake_case		,     )
      a		:   int	  =	start_length
      a		:   Union[str, Any]	  =	max_new_tokens
      a		:   Optional[int]	  =	start_length + max_new_tokens







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Union[str, Any]		,     __snake_case					:    torch.LongTensor		,     __snake_case					:    torch.FloatTensor		,     **__snake_case					:    Any      ):
      return input_ids.shape[-1] >= self.max_length

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Optional[Any]		,     __snake_case					:    float		,     __snake_case					:    Optional[float] = None      ):
      a		:   int	  =	max_time
      a		:   Dict	  =	time.time() if initial_timestamp is None else initial_timestamp







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    Optional[Any]		,     __snake_case					:    torch.LongTensor		,     __snake_case					:    torch.FloatTensor		,     **__snake_case					:    Dict      ):
      return time.time() - self.initial_timestamp > self.max_time

class 				a__(   lowerCamelCase__      ):







    @add_start_docstrings(__snake_case      )
    def __call__( self					:    str		,     __snake_case					:    torch.LongTensor		,     __snake_case					:    torch.FloatTensor		,     **__snake_case					:    List[str]      ):
      return any(criteria(__snake_case		,     __snake_case      ) for criteria in self      )







    @property
    def      lowercase_			( self					:    Dict      ):
      for stopping_criterium in self:
        if isinstance(__snake_case		,     __snake_case      ):
          return stopping_criterium.max_length
        elif isinstance(__snake_case		,     __snake_case      ):
          return stopping_criterium.max_length
      return None
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	stopping_criteria.max_length
  a		:   Union[str, Any]	  =	deepcopy(_A							)
  if stopping_max_length is not None and stopping_max_length != max_length:
    warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter'					,     _A							)
  elif stopping_max_length is None:
    new_stopping_criteria.append(MaxLengthCriteria(max_length=_A							)							)
  return new_stopping_criteria




'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )